A Biologically-Inspired Simultaneous Localization and Mapping System Based on LiDAR Sensor
This work addresses robot localization and mapping in indoor environments, representing an incremental improvement by integrating biological models with LiDAR data.
The paper tackled indoor robot navigation by developing a biologically-inspired SLAM system using LiDAR, which outperformed camera-based brain-inspired methods and was competitive with conventional LiDAR-based SLAM methods.
Simultaneous localization and mapping (SLAM) is one of the essential techniques and functionalities used by robots to perform autonomous navigation tasks. Inspired by the rodent hippocampus, this paper presents a biologically inspired SLAM system based on a LiDAR sensor using a hippocampal model to build a cognitive map and estimate the robot pose in indoor environments. Based on the biologically inspired models mimicking boundary cells, place cells, and head direction cells, the SLAM system using LiDAR point cloud data is capable of leveraging the self-motion cues from the LiDAR odometry and the boundary cues from the LiDAR boundary cells to build a cognitive map and estimate the robot pose. Experiment results show that with the LiDAR boundary cells the proposed SLAM system greatly outperforms the camera-based brain-inspired method in both simulation and indoor environments, and is competitive with the conventional LiDAR-based SLAM methods.